
import re
import os, errno
import functools
import subprocess
import numpy as np
import pandas as pd
from plotnine import *
from pandas_plink import read_plink
from warnings import filterwarnings
from matplotlib.cbook import mplDeprecation
from scipy.stats import fisher_exact, binom_test

filterwarnings("ignore",category=mplDeprecation)
filterwarnings('ignore', category=UserWarning, module='plotnine.*')
filterwarnings('ignore', category=DeprecationWarning, module='plotnine.*')


config = {
    'biomart_file': '../../../eQTL_analysis/caudate_eqtl/residualized_expression/plot_eqtls/_h/biomart.csv',
    'phenotype_file': '../../../../inputs/antipsychotics_phenotypes/_m/merged_phenotypes.csv',
    'plink_file_prefix': '../../../../inputs/genotypes/to_brnum/phase3_plink/_m/phase3',
    'gwas_snp_file': '../../../../inputs/gwas/PGC2_CLOZUK/map_phase3/_m/libd_hg38_pggc2sz_snps.tsv'
}


@functools.lru_cache()
def get_de_df():
    de_df = pd.read_csv(config_feature['de_file'], sep='\t', index_col=0)
    return de_df


@functools.lru_cache()
def get_eqtl_df(fdr=0.05):
    with subprocess.Popen('''awk ' ($6<%f) || (NR==1) {print}' %s ''' % 
                          (fdr, config_feature['matrixeqtl_output_file']),
                          shell=True, stdout=subprocess.PIPE) as p:
        eqtl_df = pd.read_csv(p.stdout, sep='\t')
    return eqtl_df


@functools.lru_cache()
def get_gwas_snps():
    return pd.read_csv(config['gwas_snp_file'], sep='\t', index_col=0, low_memory=False)


@functools.lru_cache()
def get_integration_df(fdr=0.05):
    dft = get_gwas_snps().merge(get_eqtl_df(fdr), left_on='our_snp_id', right_on='SNP', 
                                suffixes=['_PGC2', '_eqtl'])\
                         .merge(get_de_df(), left_on='gene', right_index=True)
    return dft


@functools.lru_cache()
def get_residual_expression_df():
    residual_expression_df = pd.read_csv(config_feature['residual_expression_file'], 
                                         sep='\t', index_col=0).transpose()
    return residual_expression_df


@functools.lru_cache()
def get_pheno_df():
    pheno_df = pd.read_csv(config['phenotype_file'], index_col=0)
    pheno_df['New_Dx'] = pheno_df.New_Dx.astype('category')\
                                 .cat.reorder_categories(['Control', 'Schizo_noAP', 'Schizo_AP'])
    return pheno_df


def agree_direction(row):
    return [-1, 1][row['pgc2_a1_same_as_our_counted']] * np.sign(row['OR'] - 1) * np.sign(row['t-stat']) * np.sign(row['t'])


def letter_snp(number, a0, a1):
    '''
    Example:
    letter_snp(0, 'A', 'G') is 'AA'
    letter_snp(1, 'A', 'G') is 'AG'
    letter_snp(2, 'A', 'G') is 'GG'
    
    '''
    if np.isnan(number):
        return np.nan
    if len(a0)==1 and len(a1)==1:
        sep = ''
    else:
        sep = ' '
    return sep.join(sorted([a0]*int(number) + [a1]*(2-int(number))))


def get_gwas_snp(snp_id):
    gwas = get_gwas_snps()
    r = gwas[gwas['our_snp_id']==snp_id]
    assert len(r) == 1
    return r


@functools.lru_cache()
def get_expression_and_pheno_df():
    return pd.merge(get_pheno_df(), get_residual_expression_df(), left_index=True, right_index=True)


@functools.lru_cache()
def get_plink_tuple():
    '''
    Usage: (bim, fam, bed) = get_plink_tuple()
    '''
    return read_plink(config['plink_file_prefix'])


@functools.lru_cache()
def get_snp_df(snp_id):
    '''
    Returns a dataframe containing the genotype on snp snp_id.
    The allele count is the same as in the plink files.
    
    Example: 
    get_snp_df('rs653953').head(5)
    
            rs653953_num rs653953_letter rs653953
    Br5168             0              GG    0\nGG
    Br2582             1              AG    1\nAG
    Br2378             1              AG    1\nAG
    Br5155             2              AA    2\nAA
    Br5182             2              AA    2\nAA
    '''
    (bim, fam, bed) = get_plink_tuple()
    brain_ids = list(set(get_expression_and_pheno_df()['BrNum']).intersection(set(fam['fid'])))
    snp_info = bim[bim['snp']==snp_id]
    snp_pos = snp_info.iloc[0]['i']
    fam_pos = list(fam.set_index('fid').loc[brain_ids]['i'])
    dfsnp = (pd.DataFrame(bed[[snp_pos]].compute()[:,fam_pos], columns=brain_ids, index=[snp_id + '_num'])
             .transpose().dropna())
    my_letter_snp = functools.partial(letter_snp, a0=snp_info.iloc[0]['a0'], a1=snp_info.iloc[0]['a1'])
    # the 2 - in next line is to workaround a possible bug in pandas_plink? a1 and a0 inverted
    dfsnp[[snp_id + '_num']] = 2 - dfsnp[[snp_id + '_num']].astype('int')
    dfsnp[snp_id + '_letter'] = dfsnp[snp_id + '_num'].apply(my_letter_snp)
    dfsnp[snp_id] = (dfsnp[snp_id + '_num'].astype('str') + '\n' + 
                     dfsnp[snp_id + '_letter'].astype('str')).astype('category')
    return dfsnp


@functools.lru_cache()
def get_gwas_ordered_snp_df(snp_id):
    '''
    Returns a dataframe containing the genotype on snp snp_id.
    The allele count is the number of risk alleles according to GWAS.
    
    Example: 
    get_gwas_ordered_snp_df('rs653953').head(5)
    
            rs653953_num rs653953_letter rs653953
    Br5168             2              GG    2\nGG
    Br2582             1              AG    1\nAG
    Br2378             1              AG    1\nAG
    Br5155             0              AA    0\nAA
    Br5182             0              AA    0\nAA
    '''
    pgc = get_gwas_snps()
    dfsnp = get_snp_df(snp_id).copy()
    gwas_snp = get_gwas_snp(snp_id)
    if gwas_snp['pgc2_a1_same_as_our_counted'].iloc[0]:
        if gwas_snp['OR'].iloc[0] > 1:
            pass
        else:
            dfsnp[[snp_id + '_num']] = 2 - dfsnp[[snp_id + '_num']]
    else:
        if gwas_snp['OR'].iloc[0] > 1:
            dfsnp[[snp_id + '_num']] = 2 - dfsnp[[snp_id + '_num']]
        else:
            pass
    dfsnp[snp_id] = (dfsnp[snp_id + '_num'].astype('str') + '\n' + 
                     dfsnp[snp_id + '_letter'].astype('str')).astype('category')
    return dfsnp


@functools.lru_cache()
def get_biomart_df():
    biomart = pd.read_csv(config['biomart_file'])
    biomart['description'] = biomart['description'].str.replace('\[Source.*$','')
    return biomart


@functools.lru_cache()
def get_risk_allele(snp_id):
    gwas_snp = get_gwas_snp(snp_id)
    
    if gwas_snp['OR'].iloc[0] > 1:
        ra = gwas_snp['A1'].iloc[0]
    else:
        ra = gwas_snp['A2'].iloc[0]
    
    return ra


def get_gene_symbol(gene_id, biomart=get_biomart_df()):
    ensge = re.sub('\..+$','', gene_id)
    ggg = biomart[biomart['ensembl_gene_id']==ensge]
    if ggg.shape[0]==0:
        return '', ''
    gs = ggg['external_gene_name'].values[0]
    de = ggg['description'].values[0]
    if type(de)!=str:
        de = ''
    de = re.sub('\[Source:.*$','',de)
    return gs, de


def save_plot(p, fn):
    for ext in ['png', 'pdf', 'svg']:
        p.save(fn + '.' + ext)
        

def get_snp_gene_pheno_df(snp_id, gene_id, snp_df_func):
    pheno_columns = list(get_pheno_df().columns)
    expr_df = get_expression_and_pheno_df()[pheno_columns + [gene_id]]
    snp_df =  snp_df_func(snp_id)
    return expr_df.merge(snp_df, left_on='BrNum', right_index=True)


def simple_snp_expression_pheno_plot_impl(snp_id, gene_id, snp_df_func, pheno_var):
    df = get_snp_gene_pheno_df(snp_id, gene_id, snp_df_func)
    df['Dx'] = df.Dx.astype('category').cat.rename_categories({'Control': 'CTL', 'Schizo': 'SZ'})
    y0 = df[gene_id].quantile(.01) - 0.26
    y1 = df[gene_id].quantile(.99) + 0.26
    pjd = position_jitterdodge(jitter_width=0.27)
    p = ggplot(df, aes(x=snp_id, y=gene_id, fill=pheno_var)) \
    + geom_boxplot(alpha=0.4, outlier_alpha=0) \
    + geom_jitter(position=pjd, stroke=0, alpha=0.6) + ylim(y0, y1) \
    + labs(y='Residualized expression', fill='Diagnosis') \
    + theme_matplotlib()\
    + theme(axis_text=element_text(size=18), 
            axis_title=element_text(size=21), 
            plot_title=element_text(size=22), 
            legend_text=element_text(size=14), 
            legend_title=element_text(size=16, face='bold'))
    return p


def simple_gwas_ordered_snp_expression_pheno_plot(snp_id, gene_id, pheno_var):
    return simple_snp_expression_pheno_plot_impl(snp_id, gene_id, get_gwas_ordered_snp_df, pheno_var)


def gwas_annotation(snp_id):
    return 'SZ GWAS pvalue: %.1e' % get_gwas_snp(snp_id).iloc[0]['P']


def eqtl_annotation(snp_id, gene_id):
    eqtl_df = get_eqtl_df()
    r = eqtl_df[(eqtl_df['SNP']==snp_id) & (eqtl_df['gene']==gene_id)]
    assert len(r)==1
    return 'eQTL FDR: %.1e' % r.iloc[0]['FDR']


def de_annotation(gene_id):
    de_df = get_de_df()
    g = de_df[(de_df['gencodeID'] == gene_id)]
    return 'DE adj.P.Val: %.3f' % g.iloc[0]['adj.P.Val']


def risk_allele_annotation(snp_id):
    return 'SZ risk allele: %s' % get_risk_allele(snp_id)


def gwas_annotated_eqtl_pheno_plot(snp_id, gene_id, pheno_var):
    p = simple_gwas_ordered_snp_expression_pheno_plot(snp_id, gene_id, pheno_var)                         
    gene_symbol, gene_description = get_gene_symbol(gene_id)
    
    title ="\n".join([gene_symbol, gene_id,
                      gwas_annotation(snp_id),
                      risk_allele_annotation(snp_id),
                      eqtl_annotation(snp_id, gene_id), 
                      de_annotation(gene_id)])
    
    p += ggtitle(title)
    return p


config_feature = {
    'de_file': '../../../differential_expression/_m/genes/diffExpr_szVctl_full.txt',
    'residual_expression_file': '../../../differential_expression/_m/genes/residualized_expression.tsv',
    'matrixeqtl_output_file': '../../../eQTL_analysis/caudate_eqtl/_m/cis_eqtls_genes.ctxt',
}


feature = 'genes'
try:
    os.makedirs(feature)
except OSError as e:
    if e.errno != errno.EEXIST:
        raise


dft = get_integration_df()
dft.shape

/home/jbenja13/.local/lib/python3.8/site-packages/numpy/lib/arraysetops.py:580: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison

(1739176, 44)


dft['agree_direction'] = dft.apply(agree_direction, axis=1)


agreement = {1: 'Yes', -1: 'No', 0: 0}
dft.agree_direction = [agreement[item] for item in dft['agree_direction']]


table =  [[np.sum((dft['P']<5e-8) & ((dft['adj.P.Val']<.05))),
           np.sum((dft['P']<5e-8) & ((dft['adj.P.Val']>=.05)))],
          [np.sum((dft['P']>=5e-8) & ((dft['adj.P.Val']<.05))),
           np.sum((dft['P']>=5e-8) & ((dft['adj.P.Val']>=.05)))]]
print(table)
fisher_exact(table)

[[3173, 31781], [202816, 1501406]]

(0.7390919085108425, 5.449845660955875e-63)


dft1 = dft[(dft['P']<5e-8) & ((dft['adj.P.Val']<.05))]
df = dft1.groupby('agree_direction').size().reset_index()
df


binom_test(df[0].iloc[1], df[0].sum())

2.9374167555820266e-237


dft2 = dft[(dft['P']<=5e-8)]
dft2 = dft2[(dft2['CtrlvsSZ'] != 0)]
dft2['risk_allele'] = dft2['our_snp_id'].apply(get_risk_allele)


direction = {-1: 'Down', 1: 'Up'}
boolean_conv = {True: 1, False: -1}
dft2.pgc2_a1_same_as_our_counted = [boolean_conv[item] for item in dft2['pgc2_a1_same_as_our_counted']]
dft2['eqtl_gwas_dir'] = [direction[item] for item in np.sign(dft2['pgc2_a1_same_as_our_counted']) * np.sign(dft2['t-stat']) * np.sign(dft2['OR'] - 1)]
dft2['de_dir'] = [direction[item] for item in np.sign(dft2['t'])]
dft2['eqtl_t'] = np.sign(dft2['pgc2_a1_same_as_our_counted']) * np.sign(dft2['OR'] - 1) * dft2['t-stat']

dft2 = dft2[['gene', 'Symbol', 'SNP_eqtl', 'A1', 'A2', 'risk_allele', 'OR', 
             'P', 'FDR', 'adj.P.Val', 'logFC', 't', 'eqtl_t', 
             'de_dir', 'eqtl_gwas_dir', 'agree_direction']]
dft2.to_csv('%s/integration_by_symbol.txt' % feature, sep='\t', index=False)
df2 = dft2.groupby(['gene']).first().reset_index().sort_values('P')


df2.groupby(['agree_direction']).size()

agree_direction
No     23
Yes    23
dtype: int64


df2.set_index('Symbol').rename(columns={'t': 'de_t', 'P': 'GWAS_P', 'FDR': 'eQTL_FDR', 
                                        'adj.P.Val': 'de_adj.P.Val'})


for xx in range(df2.shape[0]):
    gg = gwas_annotated_eqtl_pheno_plot(df2.iloc[xx, :].SNP_eqtl, df2.iloc[xx, :].gene, 'Dx')
    print(gg)
    label = '%s/eqtl_gwas_%s' % (feature, df2.iloc[xx, :].Symbol)
    save_plot(gg, label)

Mapping files: 100%|██████████| 3/3 [00:13<00:00,  4.48s/it]

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<ggplot: (8783883699999)>

	gene	SNP_eqtl	A1	A2	risk_allele	OR	GWAS_P	eQTL_FDR	de_adj.P.Val	logFC	de_t	eqtl_t	de_dir	eqtl_gwas_dir	agree_direction
Symbol
ZSCAN9	ENSG00000137185.11	rs35501037:27739566:T:A	T	A	T	1.259400	3.620000e-40	3.949424e-02	1.365665e-02	-0.082397	-3.337802	-3.176934	Down	Down	Yes
ZNF391	ENSG00000124613.8	rs67652222:27586220:C:T	C	T	C	1.259200	5.190000e-40	1.176320e-02	4.140334e-02	0.074953	2.862466	-3.597656	Up	Down	No
HCG4	ENSG00000176998.4	rs3117425	C	T	C	1.261400	6.890000e-39	1.794875e-03	1.468069e-02	0.224649	3.307039	4.150195	Up	Up	Yes
BRD2	ENSG00000204256.12	rs2395231:32637302:G:A	G	A	G	1.219700	6.820000e-30	1.350959e-02	2.260331e-02	-0.050859	-3.130279	-3.552720	Down	Down	Yes
FLOT1	ENSG00000137312.14	rs3130665:30735979:C:T	C	T	C	1.218400	1.100000e-29	4.112046e-02	2.923115e-02	-0.044895	-3.019341	-3.161646	Down	Down	Yes
C4A	ENSG00000244731.7	rs3131642:31450637:G:A	G	A	G	1.184600	2.080000e-28	5.615194e-09	2.714423e-03	0.392615	3.928587	6.786279	Up	Up	Yes
PPP1R10	ENSG00000204569.9	rs3132649	G	A	G	1.220100	7.580000e-27	3.958557e-02	5.739890e-03	-0.076713	-3.666350	3.176081	Down	Up	No
ZNF204P	ENSG00000204789.4	rs12179134:27675469:A:G	A	G	A	1.131700	1.580000e-22	2.159989e-02	1.394713e-02	0.097111	3.326268	-3.395166	Up	Down	No
ELFN1	ENSG00000225968.7	rs6461049	C	T	T	0.921190	1.870000e-17	1.432332e-02	4.832150e-02	-0.119752	-2.786812	-3.533485	Down	Down	Yes
EIF4E2	ENSG00000135930.13	rs10427328:233570204:T:C	T	C	T	1.076400	4.950000e-14	4.510939e-02	4.840907e-02	0.030692	2.785840	-3.126093	Up	Down	No
BAG6	ENSG00000204463.12	rs6936825:31175044:T:C	T	C	C	0.928720	2.610000e-13	3.564829e-02	1.906708e-02	-0.041937	-3.201790	3.215614	Down	Up	No
CKB	ENSG00000166165.12	rs373646693:104121939:ATTTTAT:A	A	ATTTTAT	A	1.075730	4.457000e-13	4.702242e-02	5.822810e-03	-0.082683	-3.660539	-3.109860	Down	Down	Yes
NGEF	ENSG00000066248.14	rs62193341:233708393:G:A	G	A	A	0.917580	7.130000e-13	3.111085e-02	1.511964e-02	0.109010	3.294629	-3.265640	Up	Down	No
TDRD9	ENSG00000156414.18	rs10132641:104338258:A:G	A	G	A	1.067600	5.650000e-11	1.891892e-02	3.651015e-02	0.156470	2.922234	-3.440384	Up	Down	No
AMBRA1	ENSG00000110497.14	rs7126343:46660623:T:C	T	C	C	0.920450	8.610000e-11	2.639847e-02	1.340157e-03	-0.053263	-4.159326	-3.324955	Down	Down	Yes
DRD2	ENSG00000149295.13	rs7948028:113328681:A:C	A	C	A	1.065800	3.690000e-10	4.209248e-02	6.265785e-03	0.118925	3.634433	-3.152696	Up	Down	No
NELFE	ENSG00000204356.12	rs2844479	A	C	C	0.935320	4.400000e-10	3.276454e-02	1.841820e-02	-0.048093	-3.215519	3.246682	Down	Up	No
PTN	ENSG00000105894.11	rs7785663:137070298:G:A	G	A	G	1.063700	5.530000e-10	4.939803e-02	1.314612e-02	-0.085263	-3.353056	3.090544	Down	Up	No
CACNA1I	ENSG00000100346.17	rs132585:39991496:G:A	G	A	G	1.063300	6.530000e-10	1.244871e-02	3.017872e-02	-0.096816	-3.006159	-3.579437	Down	Down	Yes
CNNM2	ENSG00000148842.17	rs4919683	C	A	C	1.060400	1.120000e-09	3.783873e-08	2.810666e-03	0.056288	3.915991	-6.453892	Up	Down	No
PCCB	ENSG00000114054.13	rs1278493	G	A	A	0.942680	1.210000e-09	3.214315e-15	4.270635e-02	-0.056763	-2.846921	-8.962383	Down	Down	Yes
CSDC2	ENSG00000172346.14	rs9611487:41460489:C:T	C	T	T	0.938400	1.610000e-09	4.260842e-02	4.433705e-02	0.133225	2.829259	3.148082	Up	Up	Yes
PLPP5	ENSG00000147535.16	rs9198:38120721:T:C	T	C	T	1.069700	2.490000e-09	2.127392e-02	3.190042e-02	-0.074217	-2.981407	-3.400386	Down	Down	Yes
ME1	ENSG00000065833.8	rs12195417:84173028:T:C	T	C	C	0.941170	3.090000e-09	2.544457e-02	2.086335e-05	0.120678	5.305498	-3.337966	Up	Down	No
TRMT61A	ENSG00000166166.12	rs201836683:104008136:CAGG:C	CAGG	C	C	0.938850	4.218000e-09	4.847909e-02	4.698102e-02	-0.062775	-2.799728	-3.097942	Down	Down	Yes
HIRIP3	ENSG00000149929.15	rs4787488	A	C	A	1.057900	4.590000e-09	8.377297e-03	4.350888e-04	-0.086237	-4.498828	3.704543	Down	Up	No
ZC3H7B	ENSG00000100403.11	rs9611522:41627924:C:T	C	T	T	0.937550	5.740000e-09	2.088513e-02	1.190576e-02	-0.057360	-3.390946	3.406728	Down	Up	No
PPTC7	ENSG00000196850.5	12:110799944:A:G	A	G	A	1.062800	7.370000e-09	8.237591e-03	1.215710e-03	0.098584	4.188957	-3.709840	Up	Down	No
TSNARE1	ENSG00000171045.14	rs72687339:143343145:C:T	C	T	C	1.074000	1.150000e-08	1.228235e-02	3.418270e-03	-0.079405	-3.854472	3.583767	Down	Up	No
PHF1	ENSG00000112511.17	rs3130018	G	A	A	0.945920	1.160000e-08	1.481843e-02	2.616096e-02	-0.045084	-3.068216	-3.522265	Down	Down	Yes
NRIP2	ENSG00000053702.14	rs10491964	A	G	G	0.941460	1.420000e-08	1.875899e-02	4.854494e-03	-0.195818	-3.728091	-3.443278	Down	Down	Yes
ENSA	ENSG00000143420.17	rs12124898:150154352:C:T	C	T	T	0.926970	1.430000e-08	3.993880e-03	1.703753e-02	0.045351	3.249546	-3.926103	Up	Down	No
KDM3B	ENSG00000120733.13	rs7703010	C	A	C	1.056300	1.590000e-08	3.170122e-02	2.841025e-02	-0.029358	-3.031244	-3.258763	Down	Down	Yes
PPM1M	ENSG00000164088.17	rs610060	G	A	A	0.947060	1.660000e-08	1.703614e-02	1.536149e-05	-0.134830	-5.376573	-3.475793	Down	Down	Yes
GRIN2A	ENSG00000183454.15	rs9930307:9899226:C:G	C	G	G	0.943860	1.830000e-08	1.299456e-02	3.804668e-04	0.166274	4.537293	-3.565376	Up	Down	No
REEP2	ENSG00000132563.15	rs10055995:137698299:C:T	C	T	C	1.056000	1.860000e-08	3.690210e-06	6.375072e-09	0.140289	6.997511	5.584240	Up	Up	Yes
PRRC2A	ENSG00000204469.12	rs9266233:31325430:G:A	G	A	A	0.943820	2.110000e-08	3.270178e-02	7.355771e-04	-0.069114	-4.340883	3.247424	Down	Up	No
PLCL1	ENSG00000115896.15	rs4389332	C	A	C	1.055000	2.150000e-08	2.151444e-02	2.197220e-02	-0.089394	-3.141100	3.396518	Down	Up	No
SREBF2	ENSG00000198911.11	rs5758487:42226395:C:T	C	T	C	1.055300	2.290000e-08	3.258331e-03	6.387156e-04	-0.079650	-4.384469	-3.984441	Down	Down	Yes
IP6K3	ENSG00000161896.11	rs597723:33694079:C:T	C	T	C	1.071700	2.460000e-08	2.805483e-03	1.618607e-03	-0.244105	-4.104238	4.026579	Down	Up	No
LLGL1	ENSG00000131899.10	rs4925114	A	G	A	1.058200	2.640000e-08	4.701305e-02	1.327660e-02	-0.098603	-3.348813	-3.109945	Down	Down	Yes
NaN	ENSG00000228944.1	rs79210963:24717969:T:C	T	C	C	0.920640	3.510000e-08	2.030916e-02	2.431347e-02	0.276255	3.100551	3.416209	Up	Up	Yes
PACSIN3	ENSG00000165912.15	rs59581888:46888001:T:TA	T	TA	TA	0.912105	3.887000e-08	3.683547e-02	2.506271e-03	-0.109838	-3.954688	3.203371	Down	Up	No
VRK2	ENSG00000028116.16	rs17815124:57991535:C:T	C	T	T	0.913190	4.090000e-08	4.222191e-02	9.980042e-03	-0.128897	-3.457907	3.151540	Down	Up	No
BNIP3L	ENSG00000104765.15	rs1564576:26190836:C:G	C	G	G	0.935950	4.270000e-08	1.612715e-02	1.877047e-02	-0.059761	-3.208755	-3.494131	Down	Down	Yes
ZNF14	ENSG00000105708.8	rs739461	T	C	T	1.061200	4.830000e-08	2.522662e-02	2.065108e-02	0.072821	3.167768	3.340966	Up	Up	Yes

Enrichment and Overlap of PGC2+CLOZUK¶

Config and Functions¶

Genes¶

Enrichment¶

Integrate DEG with PGC2+CLOZUK SNPs¶

Plot with PGC2 risk allele¶

	agree_direction	0
0	No	687
1	Yes	2486